Surgical Intelligence Can Lead to Higher Adoption of Best Practices in Minimally Invasive Surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the use of surgical intelligence for automatically monitoring critical view of safety (CVS) in laparoscopic cholecystectomy (LC) in a real-world quality initiative. BACKGROUND: Surgical intelligence encompasses routine, artificial intelligence-based capture and analysis of surgical video, and connection of derived data with patient and outcomes data. These capabilities are applied to continuously assess and improve surgical quality and efficiency in real-world settings. METHODS: Laparoscopic cholecystectomies conducted at 2 general surgery departments between December 2022 and August 2023 were routinely captured by a surgical intelligence platform, which identified and continuously presented CVS adoption, surgery duration, complexity, and negative events. In March 2023, the departments launched a quality initiative aiming for 75% CVS adoption. RESULTS: Two hundred seventy-nine procedures were performed during the study. Adoption increased from 39.2% in the 3 preintervention months to 69.2% in the final 3 months ( P < 0.001). Monthly adoption rose from 33.3% to 75.7%. Visualization of the cystic duct and artery accounted for most of the improvement; the other 2 components had high adoption throughout. Procedures with full CVS were shorter ( P = 0.007) and had fewer events ( P = 0.011) than those without. OR time decreased following intervention ( P = 0.033). CONCLUSIONS: Surgical intelligence facilitated a steady increase in CVS adoption, reaching the goal within 6 months. Low initial adoption stemmed from a single CVS component, and increased adoption was associated with improved OR efficiency. Real-world use of surgical intelligence can uncover new insights, modify surgeon behavior, and support best practices to improve surgical quality and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".